The paper, titled "Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease" used REPROCELL's human tissue assays in combination with machine learning to explore the relationship between drug response, tissue omic data, and tissue donor medical information. Ex vivo models using inflammatory bowel disease (IBD) tissues were used to measure the therapeutic efficacy of the drugs tested.
Researchers noted differences in drug response between IBD patients that varied in sex, age, or IBD type. Most interestingly, the study also linked medical histories and new genetic polymorphisms to differences in Doramapimod efficacy, one of the drugs tested during the project.
"It was incredibly exciting to work with IBM on this first-of -a-kind project," said Dr David Bunton, CEO of REPROCELL Europe and chair of the Precision Medicine Scotland Innovation Centre. "We hope that this paper will help Pharma understand the value human tissue testing can bring to their precision medicine strategies".
You can access a full copy of the article via the PLoS ONE website: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263248
Gardiner L-J, Carrieri AP, Bingham K, Macluskie G, Bunton D, McNeil M, et al. Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease. PLoS ONE 17:2 (2022).